diff --git a/TIGER/evaluate.py b/TIGER/evaluate.py
new file mode 100644
index 0000000..0dffc20
--- /dev/null
+++ b/TIGER/evaluate.py
@@ -0,0 +1,137 @@
+from lib.CoNLL_Annotation import *
+from collections import Counter
+import pandas as pd
+import numpy as np
+from sklearn.metrics import precision_recall_fscore_support as eval_f1
+from tabulate import tabulate
+import logging, argparse, sys
+from datetime import datetime
+
+
+def eval_lemma(sys, gld):
+    match, err, symbol = 0, 0, []
+    mistakes = []
+    for i, gld_tok in enumerate(gld.tokens):
+        if gld_tok.lemma == sys.tokens[i].lemma:
+            match += 1
+        elif not sys.tokens[i].lemma.isalnum(): # This was added because Turku does not lemmatize symbols (it only copies them) => ERR ((',', '--', ','), 43642)
+            symbol.append(sys.tokens[i].lemma)
+            if sys.tokens[i].word == sys.tokens[i].lemma:
+                match += 1
+            else:
+                err += 1
+        else:
+            err += 1
+            mistakes.append((gld_tok.word, gld_tok.lemma, sys.tokens[i].lemma))
+    return match, err, symbol, mistakes
+    
+
+def eval_pos(sys, gld):
+    match, mistakes = 0, []
+    y_gld, y_pred = [], []
+    for i, gld_tok in enumerate(gld.tokens):
+        y_gld.append(gld_tok.pos_tag)
+        y_pred.append(sys.tokens[i].pos_tag)
+        all_pos_labels.add(gld_tok.pos_tag)
+        if gld_tok.pos_tag == sys.tokens[i].pos_tag:
+            match += 1
+        else:
+            mistakes.append((gld_tok.word, gld_tok.pos_tag, sys.tokens[i].pos_tag))
+    return y_gld, y_pred, match, mistakes
+
+
+
+if __name__ == "__main__":
+    """
+        EVALUATIONS: 
+            python TIGER/evaluate.py -t Turku\
+                --sys_file /home/daza/datasets/TIGER_conll/tiger_turku_parsed.conllu \
+                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
+                
+            python TIGER/evaluate.py -t SpaCy\
+                --sys_file /home/daza/datasets/TIGER_conll/tiger_spacy_parsed.conllu \
+                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
+                
+            python TIGER/evaluate.py -t RNNTagger\
+                --sys_file /home/daza/datasets/TIGER_conll/tiger_all.parsed.RNNTagger.conll \
+                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
+            
+            python TIGER/evaluate.py -t TreeTagger\
+                --sys_file /home/daza/datasets/TIGER_conll/tiger_all.parsed.TreeTagger.conll \
+                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
+    """
+    
+    # =====================================================================================
+    #                    INPUT PARAMS 
+    # =====================================================================================
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-s", "--sys_file", help="System output in CoNLL-U Format", required=True)
+    parser.add_argument("-g", "--gld_file", help="Gold Labels to evaluate in CoNLL-U Format", required=True)
+    parser.add_argument("-t", "--type_sys", help="Which system produced the outputs", default="system")
+    args = parser.parse_args()
+    
+    # =====================================================================================
+    #                    LOGGING INFO ...
+    # =====================================================================================
+    logger = logging.getLogger(__name__)
+    console_hdlr = logging.StreamHandler(sys.stdout)
+    file_hdlr = logging.FileHandler(filename=f"logs/Eval_Tiger.{args.type_sys}.log")
+    logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
+    now_is = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    logger.info(f"\n\nEvaluating TIGER Corpus {now_is}")
+
+    # Read the Original TiGeR Annotations
+    gld_generator = read_conll_generator(args.gld_file, token_class=CoNLL09_Token)
+    # Read the Annotations Generated by the Automatic Parser [Turku, SpaCy, RNNTagger] 
+    if args.type_sys == "RNNTagger":
+        sys_generator = read_conll_generator(args.sys_file, token_class=RNNTagger_Token)
+    elif args.type_sys == "TreeTagger":
+        sys_generator = read_conll_generator(args.sys_file, token_class=RNNTagger_Token, sent_sep="</S>")
+    else:
+        sys_generator = read_conll_generator(args.sys_file, token_class=CoNLLUP_Token)
+    
+    lemma_all_match, lemma_all_err, lemma_all_mistakes = 0, 0, []
+    lemma_all_symbols = []
+    pos_all_match, pos_all_err, pos_all_mistakes = 0, 0, []
+    pos_all_pred, pos_all_gld = [], []
+    all_pos_labels = set()
+    
+    for i, (s,g) in enumerate(zip(sys_generator, gld_generator)):
+        # print([x.word for x in s.tokens])
+        # print([x.word for x in g.tokens])
+        assert len(s.tokens) == len(g.tokens), f"Token Mismatch! S={len(s.tokens)} G={len(g.tokens)} IX={i+1}"
+        # Lemmas ...
+        lemma_match, lemma_err, lemma_sym, mistakes = eval_lemma(s,g)
+        lemma_all_match += lemma_match
+        lemma_all_err += lemma_err
+        lemma_all_mistakes += mistakes
+        lemma_all_symbols += lemma_sym
+        # POS Tags ...
+        pos_gld, pos_pred, pos_match, pos_mistakes = eval_pos(s, g)
+        pos_all_pred += pos_pred 
+        pos_all_gld += pos_gld
+        pos_all_match +=  pos_match
+        pos_all_err += len(pos_mistakes)
+        pos_all_mistakes += pos_mistakes
+    
+    # Lemmas ...
+    logger.info(f"Lemma Matches = {lemma_all_match} || Errors = {lemma_all_err} || Symbol Chars = {len(lemma_all_symbols)}")
+    logger.info(f"Lemma Accuracy = {(lemma_all_match*100/(lemma_all_match + lemma_all_err)):.2f}%\n")
+    lemma_miss_df = pd.DataFrame(lemma_all_mistakes, columns =['Gold_Word', 'Gold_Lemma', 'Sys_Lemma']).value_counts()
+    lemma_miss_df.to_csv(path_or_buf=f"outputs/LemmaErrors.{args.type_sys}.tsv", sep="\t")
+    
+    # POS Tags ...
+    logger.info(f"POS Matches = {pos_all_match} || Errors = {pos_all_err}")
+    logger.info(f"POS Tagging Accuracy = {(pos_all_match*100/(pos_all_match + pos_all_err)):.2f}%\n")
+    pos_miss_df = pd.DataFrame(pos_all_mistakes, columns =['Gold_Word', 'Gold_POS', 'Sys_POS']).value_counts()
+    pos_miss_df.to_csv(path_or_buf=f"outputs/POS-Errors.{args.type_sys}.tsv", sep="\t")
+    
+    ordered_labels = sorted(all_pos_labels)
+    p_labels, r_labels, f_labels, support = eval_f1(y_true=pos_all_gld, y_pred=pos_all_pred, labels=ordered_labels , average=None)
+    scores_per_label = zip(ordered_labels, [x*100 for x in p_labels], [x*100 for x in r_labels], [x*100 for x in f_labels])
+    logger.info("\n\n")
+    logger.info(tabulate(scores_per_label, headers=["POS Tag","Precision", "Recall", "F1"], floatfmt=".2f"))
+    p_labels, r_labels, f_labels, support = eval_f1(y_true=np.array(pos_all_gld), y_pred=np.array(pos_all_pred), average='micro', zero_division=0)
+    logger.info(f"Total Prec = {p_labels*100}\tRec = {r_labels*100}\tF1 = {f_labels*100}")
+    
+    
\ No newline at end of file
diff --git a/TIGER/parse_spacy.py b/TIGER/parse_spacy.py
new file mode 100644
index 0000000..de82917
--- /dev/null
+++ b/TIGER/parse_spacy.py
@@ -0,0 +1,87 @@
+import spacy
+from spacy.tokens import Doc
+import logging, sys, time
+from lib.CoNLL_Annotation import CoNLL09_Token
+import my_utils.file_utils as fu
+from germalemma import GermaLemma
+
+
+TIGER_CORPUS = "/home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09"
+OUTPUT_FILE = "/home/daza/datasets/TIGER_conll/tiger_spacy_parsed.conllu"
+TEXT_OUTPUT = "/home/daza/datasets/TIGER_conll/tiger_all.txt"
+
+
+class WhitespaceTokenizer(object):
+	def __init__(self, vocab):
+		self.vocab = vocab
+
+	def __call__(self, text):
+		words = text.split(' ')
+		# All tokens 'own' a subsequent space character in this tokenizer
+		spaces = [True] * len(words)
+		return Doc(self.vocab, words=words, spaces=spaces)
+
+
+def get_conll_str(spacy_doc):
+	conll_lines = [] # We want: [ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC]
+	for ix, token in enumerate(spacy_doc):
+		content = (str(ix), token.text, find_germalemma(token.text, token.tag_, token.lemma_), token.pos_, token.tag_, "_", "_", "_", "_", "_")
+		conll_lines.append("\t".join(content))
+	return "\n".join(conll_lines)
+
+
+# def freeling_lemma_lookup():
+# 	dicts_path = "/home/daza/Frameworks/FreeLing/data/de/dictionary/entries/"
+	
+def find_germalemma(word, pos, spacy_lemma):
+	simplify_pos = {"ADJA":"ADJ", "ADJD":"ADJ",
+					"NA":"N", "NE":"N", "NN":"N",
+					"ADV":"ADV", "PAV":"ADV", "PROAV":"ADV", "PAVREL":"ADV", "PWAV":"ADV", "PWAVREL":"ADV",
+					"VAFIN":"V", "VAIMP":"V", "VAINF":"V", "VAPP":"V", "VMFIN":"V", "VMINF":"V",
+					"VMPP":"V", "VVFIN":"V", "VVIMP":"V", "VVINF":"V", "VVIZU":"V","VVPP":"V"
+				}
+	# simplify_pos = {"VERB": "V", "ADV": "ADV", "ADJ": "ADJ", "NOUN":"N", "PROPN": "N"}
+	try:
+		return lemmatizer.find_lemma(word, simplify_pos.get(pos, "UNK"))
+	except:
+		return spacy_lemma
+
+
+if __name__ == "__main__":
+	file_has_next, chunk_ix = True, 0
+	CHUNK_SIZE = 10000
+	
+	# =====================================================================================
+	#                    LOGGING INFO ...
+	# =====================================================================================
+	logger = logging.getLogger(__name__)
+	console_hdlr = logging.StreamHandler(sys.stdout)
+	file_hdlr = logging.FileHandler(filename=f"logs/Parse_Tiger.SpaCy.log")
+	logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
+	logger.info(f"Chunking TIGER Corpus in chunks of {CHUNK_SIZE} Sentences")
+	
+	# =====================================================================================
+	#                    POS TAG DOCUMENTS
+	# =====================================================================================
+	spacy_de = spacy.load("de_core_news_lg", disable=["ner", "parser"])
+	spacy_de.tokenizer = WhitespaceTokenizer(spacy_de.vocab) # We won't re-tokenize to respect how the source CoNLL are tokenized!
+	write_out = open(OUTPUT_FILE, "w")
+	lemmatizer = GermaLemma()
+	write_plain = open(TEXT_OUTPUT, "w")
+	
+	start = time.time()
+	total_processed_sents = 0
+	line_generator = fu.file_generator(TIGER_CORPUS)
+	while file_has_next:
+		sents, gld, file_has_next = fu.get_file_text_chunk(line_generator, chunk_size=CHUNK_SIZE, token_class=CoNLL09_Token)
+		total_processed_sents += len(sents)
+		logger.info(f"Already processed {total_processed_sents} sentences...")
+		for doc in spacy_de.pipe(sents, batch_size=1000, n_process=10):
+			conll_str = get_conll_str(doc)
+			write_out.write(conll_str)
+			write_out.write("\n\n")
+			write_plain.write(" ".join([x.text for x in doc])+"\n") # OPTIONAL: This can be commented for efficiency...
+			
+	end = time.time()
+	logger.info(f"Processing File {TIGER_CORPUS} took {(end - start)} seconds!")
+			
\ No newline at end of file
diff --git a/TIGER/tiger_parse_turku.py b/TIGER/parse_turku.py
similarity index 79%
rename from TIGER/tiger_parse_turku.py
rename to TIGER/parse_turku.py
index 6ccb64a..140ea0c 100644
--- a/TIGER/tiger_parse_turku.py
+++ b/TIGER/parse_turku.py
@@ -2,8 +2,8 @@
 import subprocess, json, time
 import requests, glob, logging
 import os.path, sys
-from CoNLL_Annotation import CoNLL09_Token
-from my_utils import *
+from lib.CoNLL_Annotation import CoNLL09_Token
+import my_utils.file_utils as fu
 
 
 TIGER_CORPUS = "/home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09"
@@ -18,7 +18,7 @@
     # =====================================================================================
     logger = logging.getLogger(__name__)
     console_hdlr = logging.StreamHandler(sys.stdout)
-    file_hdlr = logging.FileHandler(filename=f"ParseTests.log")
+    file_hdlr = logging.FileHandler(filename=f"logs/Parse_Tiger_Turku.log")
     logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
     logger.info(f"Chunking TIGER Corpus in chunks of {CHUNK_SIZE} Sentences")
     
@@ -27,12 +27,12 @@
     # =====================================================================================
     start = time.time()
     total_processed_sents = 0
-    line_generator = file_generator(TIGER_CORPUS)
+    line_generator = fu.file_generator(TIGER_CORPUS)
     while file_has_next:
-        raw_text, file_has_next, n_sents = get_file_chunk(line_generator, chunk_size=CHUNK_SIZE, token_class=CoNLL09_Token)
+        raw_text, file_has_next, n_sents = fu.get_file_chunk(line_generator, chunk_size=CHUNK_SIZE, token_class=CoNLL09_Token)
         total_processed_sents += n_sents
         if len(raw_text) > 0:
-            turku_parse_file(raw_text, TIGER_CORPUS, chunk_ix)
+            fu.turku_parse_file(raw_text, TIGER_CORPUS, chunk_ix)
             now = time.time()
             elapsed = (now - start)
             logger.info(f"Time Elapsed: {elapsed}. Processed {total_processed_sents}. [{total_processed_sents/elapsed} Sents/sec]\n") # Toks/Sec???
diff --git a/TIGER/tiger_evaluate.py b/TIGER/tiger_evaluate.py
deleted file mode 100644
index e89e277..0000000
--- a/TIGER/tiger_evaluate.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from CoNLL_Annotation import *
-from collections import Counter
-import pandas as pd
-import numpy as np
-from sklearn.metrics import precision_recall_fscore_support as eval_f1
-from tabulate import tabulate
-
-
-def eval_lemma(sys, gld):
-    match, err, symbol = 0, 0, []
-    mistakes = []
-    for i, gld_tok in enumerate(gld.tokens):
-        if gld_tok.lemma == sys.tokens[i].lemma:
-            match += 1
-        elif not sys.tokens[i].lemma.isalnum(): # This was added because Turku does not lemmatize symbols (it only copies them) => ERR ((',', '--', ','), 43642)
-            symbol.append(sys.tokens[i].lemma)
-            if sys.tokens[i].word == sys.tokens[i].lemma:
-                match += 1
-            else:
-                err += 1
-        else:
-            err += 1
-            mistakes.append((gld_tok.word, gld_tok.lemma, sys.tokens[i].lemma))
-    return match, err, symbol, mistakes
-    
-
-def eval_pos(sys, gld):
-    match, mistakes = 0, []
-    y_gld, y_pred = [], []
-    for i, gld_tok in enumerate(gld.tokens):
-        y_gld.append(gld_tok.pos_tag)
-        y_pred.append(sys.tokens[i].pos_tag)
-        all_pos_labels.add(gld_tok.pos_tag)
-        all_pos_labels.add(sys.tokens[i].pos_tag)
-        if gld_tok.pos_tag == sys.tokens[i].pos_tag:
-            match += 1
-        else:
-            mistakes.append((gld_tok.word, gld_tok.pos_tag, sys.tokens[i].pos_tag))
-    return y_gld, y_pred, match, mistakes
-
-
-
-if __name__ == "__main__":
-
-    # Read the Original TiGeR Annotations
-    gld_filename = "/home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09"
-    gld_generator = read_conll_generator(gld_filename, token_class=CoNLL09_Token)
-    
-    # Read the Annotations Generated by the Automatic Parser [Turku] 
-    sys_filename = "/home/daza/datasets/TIGER_conll/tiger_turku_parsed.conllu"
-    sys_generator = read_conll_generator(sys_filename, token_class=CoNLLUP_Token)
-    
-    lemma_all_match, lemma_all_err, lemma_all_mistakes = 0, 0, []
-    lemma_all_symbols = []
-    pos_all_match, pos_all_err, pos_all_mistakes = 0, 0, []
-    pos_all_pred, pos_all_gld = [], []
-    all_pos_labels = set()
-    
-    for i, (s,g) in enumerate(zip(sys_generator, gld_generator)):
-        assert len(s.tokens) == len(g.tokens), "Token Mismatch!"
-        # Lemmas ...
-        lemma_match, lemma_err, lemma_sym, mistakes = eval_lemma(s,g)
-        lemma_all_match += lemma_match
-        lemma_all_err += lemma_err
-        lemma_all_mistakes += mistakes
-        lemma_all_symbols += lemma_sym
-        # POS Tags ...
-        pos_gld, pos_pred, pos_match, pos_mistakes = eval_pos(s, g)
-        pos_all_pred += pos_pred 
-        pos_all_gld += pos_gld
-        pos_all_match +=  pos_match
-        pos_all_err += len(pos_mistakes)
-        pos_all_mistakes += pos_mistakes
-    
-    # Lemmas ...
-    print(f"Lemma Matches = {lemma_all_match} || Errors = {lemma_all_err} || Symbol Chars = {len(lemma_all_symbols)}")
-    print(f"Lemma Accuracy = {(lemma_all_match*100/(lemma_all_match + lemma_all_err)):.2f}%\n")
-    lemma_miss_df = pd.DataFrame(lemma_all_mistakes, columns =['Gold_Word', 'Gold_Lemma', 'Sys_Lemma']).value_counts()
-    lemma_miss_df.to_csv(path_or_buf="LemmaErrors.tsv", sep="\t")
-    
-    # POS Tags ...
-    print(f"POS Matches = {pos_all_match} || Errors = {pos_all_err}")
-    print(f"POS Tagging Accuracy = {(pos_all_match*100/(pos_all_match + pos_all_err)):.2f}%\n")
-    pos_miss_df = pd.DataFrame(pos_all_mistakes, columns =['Gold_Word', 'Gold_POS', 'Sys_POS']).value_counts()
-    pos_miss_df.to_csv(path_or_buf="POS-Errors.tsv", sep="\t")
-    
-    ordered_labels = sorted(all_pos_labels)
-    p_labels, r_labels, f_labels, support = eval_f1(y_true=pos_all_gld, y_pred=pos_all_pred, labels=ordered_labels , average=None)
-    scores_per_label = zip(ordered_labels, [x*100 for x in p_labels], [x*100 for x in r_labels], [x*100 for x in f_labels])
-    print("\n\n")
-    print(tabulate(scores_per_label, headers=["POS Tag","Precision", "Recall", "F1"], floatfmt=".2f"))
-    print("\n Total Prec, Rec, and F1 Score: ")
-    p_labels, r_labels, f_labels, support = eval_f1(y_true=np.array(pos_all_gld), y_pred=np.array(pos_all_pred), average='micro', zero_division=0)
-    print(p_labels*100, r_labels*100, f_labels*100)
-    
-    
\ No newline at end of file
diff --git a/TIGER/tiger_parse_spacy.py b/TIGER/tiger_parse_spacy.py
deleted file mode 100644
index e69de29..0000000
--- a/TIGER/tiger_parse_spacy.py
+++ /dev/null